Matthew E. Peters
E800453
Matthew E. Peters is a computer scientist and researcher known for co-developing the ELMo deep contextualized word representation model in natural language processing.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Matthew E. Peters canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8993047 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew E. Peters Context triple: [Elmo, introducedBy, Matthew E. Peters]
-
A.
Matthew B. Roberts
Matthew B. Roberts is a television writer and producer best known for his leading creative role on the historical drama series "Outlander."
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B.
Craig A. Stough
Craig A. Stough is an American local government leader who serves as the mayor of Sylvania, Ohio.
-
C.
Michael T. Williamson
Michael T. Williamson is an American actor best known for his role as Benjamin Buford "Bubba" Blue in the film Forrest Gump.
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D.
Mike Peterson
Mike Peterson is a former American football linebacker who played in the NFL, primarily for the Jacksonville Jaguars and Atlanta Falcons, and later became a college football coach.
-
E.
Stephen M. Kellen
Stephen M. Kellen was a prominent financier and philanthropist known for his leadership at Arnhold and S. Bleichroeder and his significant support of cultural and educational institutions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew E. Peters Target entity description: Matthew E. Peters is a computer scientist and researcher known for co-developing the ELMo deep contextualized word representation model in natural language processing.
-
A.
Matthew B. Roberts
Matthew B. Roberts is a television writer and producer best known for his leading creative role on the historical drama series "Outlander."
-
B.
Craig A. Stough
Craig A. Stough is an American local government leader who serves as the mayor of Sylvania, Ohio.
-
C.
Michael T. Williamson
Michael T. Williamson is an American actor best known for his role as Benjamin Buford "Bubba" Blue in the film Forrest Gump.
-
D.
Mike Peterson
Mike Peterson is a former American football linebacker who played in the NFL, primarily for the Jacksonville Jaguars and Atlanta Falcons, and later became a college football coach.
-
E.
Stephen M. Kellen
Stephen M. Kellen was a prominent financier and philanthropist known for his leadership at Arnhold and S. Bleichroeder and his significant support of cultural and educational institutions.
- F. None of above. chosen
Statements (27)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
researcher ⓘ |
| approach | bidirectional language modeling for word representations ⓘ |
| citationsForWork | Deep contextualized word representations is highly cited in NLP research NERFINISHED ⓘ |
| coAuthorOf | Deep contextualized word representations NERFINISHED ⓘ |
| coDeveloperOf | ELMo NERFINISHED ⓘ |
| contributedTo | advances in word representation models ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ natural language processing ⓘ |
| impact |
ELMo became a standard baseline for contextual word embeddings
NERFINISHED
ⓘ
ELMo improved performance on multiple NLP benchmarks NERFINISHED ⓘ |
| influenced |
contextual embedding methods in NLP
ⓘ
pretrained language model research ⓘ |
| knownFor |
ELMo
NERFINISHED
ⓘ
contextual word embeddings ⓘ deep contextualized word representations ⓘ |
| publicationTitle | Deep contextualized word representations ⓘ |
| publishedIn | NAACL 2018 NERFINISHED ⓘ |
| researchArea |
neural networks for language
ⓘ
sequence modeling ⓘ text representation ⓘ |
| usedMethods |
language model pretraining
ⓘ
recurrent neural networks ⓘ |
| workFocus |
neural language models
ⓘ
representation learning for NLP ⓘ transfer learning in NLP ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Matthew E. Peters Description of subject: Matthew E. Peters is a computer scientist and researcher known for co-developing the ELMo deep contextualized word representation model in natural language processing.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.